1. Field of the Disclosure
The present innovation relates generally to computer vision and machine learning using artificial neural networks, and more particularly in one exemplary aspect to computer apparatus and methods for processing of sensory data using pulse-code neural networks.
2. Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011, and U.S. patent application Ser. No. 13/152,119, Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, each incorporated herein by reference in its entirety.
Typically, artificial spiking neural networks, such as the network described in owned U.S. patent application Ser. No. 13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORK APPARATUS AND METHODS” and incorporated herein by reference in its entirety, may comprise a plurality of units (or nodes), which correspond to neurons in a biological neural network. Any given unit may be connected to many other units via connections, also referred to as communications channels, and/or synaptic connections. The units providing inputs to any given unit are commonly referred to as the pre-synaptic units, while the unit receiving the inputs is referred to as the post-synaptic unit.
Each of the unit-to-unit connections may be assigned, inter alia, a connection efficacy, which in general may refer to a magnitude and/or probability of input spike influence on unit output response (i.e., output spike generation/firing). The efficacy may comprise, for example a parameter—synaptic weight—by which one or more state variables of post-synaptic unit are changed. During operation of a pulse-code network, synaptic weights may be dynamically adjusted using what is referred to as the spike-timing dependent plasticity (STDP) in order to implement, among other things, network learning. In some implementations, larger weights may be associated with a greater effect a synapse has on the activity of the post-synaptic neuron.
In some prior art visual processing implementations, such as illustrated in FIG. 1, a spiking neuron network in may comprise one or more identical neurons. During operation, the neurons of the network may evolve based on the neuron adaptation process and the input, illustrated by the frame 102 in FIG. 1. The input may comprise one or more objects and/or features 104, 106. Some of the features may be less frequently present in the input (e.g., the horizontal feature 104 in FIG. 1A) compared to other features (e.g., such as vertical features 106 in FIG. 1). In some applications it may be of benefit to quickly identify salient (e.g., less frequent) features in order to, for example, instruct a saccading module of a visual processing system to transition (saccade) to the salient feature. Such salient feature selection may also be referred to as formation of a “pop-out”.
As a result of network operation, the neuron population of the network may evolve to comprise two (or more) different types of neurons, as depicted by squares 116 and circles 114 in FIG. 1. The neurons of different types may be randomly distributed and randomly connected via connections 118, as illustrated by the network snapshots shown in the panels 110, 140 in FIG. 1. The neurons 104, 106 of the network may evolve to form receptive fields, e.g., depicted by broken line rectangles in FIG. 1. As the vertical features are more frequent in the input 102, more of the receptive fields may select a vertical feature, as shown by the receptive field map 120 in FIG. 1. In some realizations, the receptive fields may fail to form, and/or may select the wrong objects (e.g., the background shown in the panel 150). The receptive field map 150 of FIG. 1 does not contain a pop-out.
Some existing approaches utilize re-wiring of the network during operation in order to, for example, identify salient features (cause pop-outs). However, such processes may be computationally intensive, as the network composition may change rapidly based on the input. Accordingly, manual identification rewiring of a network comprising many neurons of different types that is capable of real time visual processing may require computational power that exceeds the capability of video processing devices, particularly portable devices.
Consequently, there is a need for an improved plasticity mechanism to adaptively affect network connectivity in order to realize a spiking neuron network capable of reliably identifying salient features within a wide variety of inputs.